用机器学习从病历中挖掘偏头痛患者中风的潜在病因
Cryptogenic stroke and migraine: using probabilistic independence and machine learning to uncover latent sources of disease from the electronic health record
- 通过概率独立性分析提取病历中的隐藏风险因素
- 模型预测10年中风风险准确率达0.771（ROC）
- 发现药物干预最关键，过敏性鼻炎或为潜在诱因
偏头痛是常见但复杂的神经系统疾病，使终生隐源性中风（CS）风险翻倍。然而该关系尚不明确，临床指南也较少。本文提出一种数据驱动方法，从电子健康记录（EHR）中提取概率独立的潜在来源，并构建偏头痛患者10年中风风险预测模型。这些来源代表作用于EHR因果图的外部潜变量，近似反映本群体中风的根源。基于这些来源训练的随机森林模型表现出良好性能（ROC 0.771），并识别出对中风最具预测力的前10个因素。结果显示，药物干预是降低中风风险最关键的要素，同时发现与过敏性鼻炎相关的因素可能是偏头痛患者中风的潜在病因。
原文摘要 · Abstract (English)
Migraine is a common but complex neurological disorder that doubles the lifetime risk of cryptogenic stroke (CS). However, this relationship remains poorly characterized, and few clinical guidelines exist to reduce this associated risk. We therefore propose a data-driven approach to extract probabilistically-independent sources from electronic health record (EHR) data and create a 10-year risk-predictive model for CS in migraine patients. These sources represent external latent variables acting on the causal graph constructed from the EHR data and approximate root causes of CS in our population. A random forest model trained on patient expressions of these sources demonstrated good accuracy (ROC 0.771) and identified the top 10 most predictive sources of CS in migraine patients. These sources revealed that pharmacologic interventions were the most important factor in minimizing CS risk in our population and identified a factor related to allergic rhinitis as a potential causative source of CS in migraine patients.
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